{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习工程师纳米学位\n",
    "## 模型评价与验证\n",
    "## 项目 1: 预测波士顿房价\n",
    "\n",
    "\n",
    "欢迎来到机器学习工程师纳米学位的第一个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能来让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**'练习'**开始的标题表示接下来的内容中有需要你必须实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**'TODO'**标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还**必须**回答一些与项目和实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。你的项目将会根据你对问题的回答和撰写代码所实现的功能来进行评分。\n",
    "\n",
    ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始\n",
    "在这个项目中，你将利用马萨诸塞州波士顿郊区的房屋信息数据训练和测试一个模型，并对模型的性能和预测能力进行测试。通过该数据训练后的好的模型可以被用来对房屋做特定预测---尤其是对房屋的价值。对于房地产经纪等人的日常工作来说，这样的预测模型被证明非常有价值。\n",
    "\n",
    "此项目的数据集来自[UCI机器学习知识库](https://archive.ics.uci.edu/ml/datasets/Housing)。波士顿房屋这些数据于1978年开始统计，共506个数据点，涵盖了麻省波士顿不同郊区房屋14种特征的信息。本项目对原始数据集做了以下处理：\n",
    "- 有16个`'MEDV'` 值为50.0的数据点被移除。 这很可能是由于这些数据点包含**遗失**或**看不到的值**。\n",
    "- 有1个数据点的 `'RM'` 值为8.78. 这是一个异常值，已经被移除。\n",
    "- 对于本项目，房屋的`'RM'`， `'LSTAT'`，`'PTRATIO'`以及`'MEDV'`特征是必要的，其余不相关特征已经被移除。\n",
    "- `'MEDV'`特征的值已经过必要的数学转换，可以反映35年来市场的通货膨胀效应。\n",
    "\n",
    "运行下面区域的代码以载入波士顿房屋数据集，以及一些此项目所需的Python库。如果成功返回数据集的大小，表示数据集已载入成功。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston housing dataset has 489 data points with 4 variables each.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
       "      <td>4.98</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.421</td>\n",
       "      <td>9.14</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.185</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.998</td>\n",
       "      <td>2.94</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.147</td>\n",
       "      <td>5.33</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RM  LSTAT  PTRATIO\n",
       "0  6.575   4.98     15.3\n",
       "1  6.421   9.14     17.8\n",
       "2  7.185   4.03     17.8\n",
       "3  6.998   2.94     18.7\n",
       "4  7.147   5.33     18.7"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "# 载入此项目所需要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import visuals as vs # Supplementary code\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "# Pretty display for notebooks\n",
    "# 让结果在notebook中显示\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the Boston housing dataset\n",
    "# 载入波士顿房屋的数据集\n",
    "data = pd.read_csv('housing.csv')\n",
    "prices = data['MEDV']\n",
    "features = data.drop('MEDV', axis = 1)\n",
    "\n",
    "# Success\n",
    "# 完成\n",
    "print \"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape)\n",
    "\n",
    "data.head()\n",
    "\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析数据\n",
    "在项目的第一个部分，你会对波士顿房地产数据进行初步的观察并给出你的分析。通过对数据的探索来熟悉数据可以让你更好地理解和解释你的结果。\n",
    "\n",
    "由于这个项目的最终目标是建立一个预测房屋价值的模型，我们需要将数据集分为**特征(features)**和**目标变量(target variable)**。**特征** `'RM'`， `'LSTAT'`，和 `'PTRATIO'`，给我们提供了每个数据点的数量相关的信息。**目标变量**：` 'MEDV'`，是我们希望预测的变量。他们分别被存在`features`和`prices`两个变量名中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 练习：基础统计运算\n",
    "你的第一个编程练习是计算有关波士顿房价的描述统计数据。我们已为你导入了` numpy `，你需要使用这个库来执行必要的计算。这些统计数据对于分析模型的预测结果非常重要的。\n",
    "在下面的代码中，你要做的是：\n",
    "- 计算`prices`中的`'MEDV'`的最小值、最大值、均值、中值和标准差；\n",
    "- 将运算结果储存在相应的变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for Boston housing dataset:\n",
      "\n",
      "Minimum price: $105,000.00\n",
      "Maximum price: $1,024,800.00\n",
      "Mean price: $454,342.94\n",
      "Median price $438,900.00\n",
      "Standard deviation of prices: $165,171.13\n"
     ]
    }
   ],
   "source": [
    "# TODO: Minimum price of the data\n",
    "#目标：计算价值的最小值\n",
    "minimum_price = np.min(prices)\n",
    "\n",
    "# TODO: Maximum price of the data\n",
    "#目标：计算价值的最大值\n",
    "maximum_price = np.max(prices)\n",
    "\n",
    "# TODO: Mean price of the data\n",
    "#目标：计算价值的平均值\n",
    "mean_price = np.mean(prices)\n",
    "\n",
    "# TODO: Median price of the data\n",
    "#目标：计算价值的中值\n",
    "median_price = np.median(prices)\n",
    "\n",
    "# TODO: Standard deviation of prices of the data\n",
    "#目标：计算价值的标准差\n",
    "std_price = np.std(prices)\n",
    "\n",
    "# Show the calculated statistics\n",
    "#目标：输出计算的结果\n",
    "print \"Statistics for Boston housing dataset:\\n\"\n",
    "print \"Minimum price: ${:,.2f}\".format(minimum_price)\n",
    "print \"Maximum price: ${:,.2f}\".format(maximum_price)\n",
    "print \"Mean price: ${:,.2f}\".format(mean_price)\n",
    "print \"Median price ${:,.2f}\".format(median_price)\n",
    "print \"Standard deviation of prices: ${:,.2f}\".format(std_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题1 - 特征观察\n",
    "\n",
    "如前文所述，本项目中我们关注的是其中三个值:`'RM'`、`'LSTAT'` 和`'PTRATIO'`，对每一个数据点:\n",
    "- `'RM'` 是该地区中每个房屋的平均房间数量；\n",
    "- `'LSTAT'` 是指该地区有多少百分比的房东属于是低收入阶层（有工作但收入微薄）；\n",
    "- `'PTRATIO'` 是该地区的中学和小学里，学生和老师的数目比（`学生/老师`）。\n",
    "\n",
    "_凭直觉，上述三个特征中对每一个来说，你认为增大该特征的数值，`'MEDV'`的值会是**增大**还是**减小**呢？每一个答案都需要你给出理由。_\n",
    "\n",
    "**提示：**你预期一个`'RM'` 值是6的房屋跟`'RM'` 值是7的房屋相比，价值更高还是更低呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答** \n",
    "1. 'MEDV'与'RM'成正比，因为房屋的房间数量越多肯定价格越贵。\n",
    "2. 'MEDV'与'LSTAT'成反比，因为低收入人群越多，房价应该越低。\n",
    "3. 'MEDV'与'PTRATIO'成反比，因为学生/老师比越大，说明教育资源越紧缺，教育质量越堪忧，因此人们来这的欲望也越低，房价也就越低。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模\n",
    "在项目的第二部分中，你需要了解必要的工具和技巧来让你的模型进行预测。用这些工具和技巧对每一个模型的表现做精确的衡量可以极大地增强你预测的信心。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：定义衡量标准\n",
    "如果不能对模型的训练和测试的表现进行量化地评估，我们就很难衡量模型的好坏。通常我们会定义一些衡量标准，这些标准可以通过对某些误差或者拟合程度的计算来得到。在这个项目中，你将通过运算[*决定系数*](http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination) R<sup>2</sup> 来量化模型的表现。模型的决定系数是回归分析中十分常用的统计信息，经常被当作衡量模型预测能力好坏的标准。\n",
    "\n",
    "R<sup>2</sup>的数值范围从0至1，表示**目标变量**的预测值和实际值之间的相关程度平方的百分比。一个模型的R<sup>2</sup> 值为0还不如直接用**平均值**来预测效果好；而一个R<sup>2</sup> 值为1的模型则可以对目标变量进行完美的预测。从0至1之间的数值，则表示该模型中目标变量中有百分之多少能够用**特征**来解释。_模型也可能出现负值的R<sup>2</sup>，这种情况下模型所做预测有时会比直接计算目标变量的平均值差很多。_\n",
    "\n",
    "在下方代码的 `performance_metric` 函数中，你要实现：\n",
    "- 使用 `sklearn.metrics` 中的 `r2_score` 来计算 `y_true` 和 `y_predict`的R<sup>2</sup>值，作为对其表现的评判。\n",
    "- 将他们的表现评分储存到`score`变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def performance_metric(y_true, y_predict):\n",
    "    \"\"\" Calculates and returns the performance score between \n",
    "        true and predicted values based on the metric chosen. \"\"\"\n",
    "    \n",
    "    # TODO: Calculate the performance score between 'y_true' and 'y_predict'\n",
    "    score = r2_score(y_true, y_predict)\n",
    "    \n",
    "    # Return the score\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题2 - 拟合程度\n",
    "\n",
    "假设一个数据集有五个数据且一个模型做出下列目标变量的预测：\n",
    "\n",
    "| 真实数值 | 预测数值 |\n",
    "| :-------------: | :--------: |\n",
    "| 3.0 | 2.5 |\n",
    "| -0.5 | 0.0 |\n",
    "| 2.0 | 2.1 |\n",
    "| 7.0 | 7.8 |\n",
    "| 4.2 | 5.3 |\n",
    "*你会觉得这个模型已成功地描述了目标变量的变化吗？如果成功，请解释为什么，如果没有，也请给出原因。*  \n",
    "\n",
    "运行下方的代码，使用`performance_metric`函数来计算模型的决定系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has a coefficient of determination, R^2, of 0.923.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d2af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the performance of this model\n",
    "test_id = np.arange(1, 6)\n",
    "test_true = [3, -0.5, 2, 7, 4.2]\n",
    "test_predict = [2.5, 0.0, 2.1, 7.8, 5.3]\n",
    "score = performance_metric(test_true, test_predict)\n",
    "print \"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(test_id, test_true, marker='o', label='true')\n",
    "plt.scatter(test_id, test_predict, marker='x', label='predict')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答**:\n",
    "- 由于$R^2=0.923$，可以知道模型拟合的比较好，也就是说模型可以解释92.3%的真实值。\n",
    "- 另一方面，通过画图对比两个数据集的分布趋势（如上图所示），可以看到预测点基本上都在真实值附近，因此该模型拟合的挺好的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 数据分割与重排\n",
    "接下来，你需要把波士顿房屋数据集分成训练和测试两个子集。通常在这个过程中，数据也会被重新排序，以消除数据集中由于排序而产生的偏差。\n",
    "在下面的代码中，你需要：\n",
    "- 使用 `sklearn.model_selection` 中的 `train_test_split`， 将`features`和`prices`的数据都分成用于训练的数据子集和用于测试的数据子集。\n",
    "  - 分割比例为：80%的数据用于训练，20%用于测试；\n",
    "  - 选定一个数值以设定 `train_test_split` 中的 `random_state` ，这会确保结果的一致性；\n",
    "- 最终分离出的子集为`X_train`,`X_test`,`y_train`,和`y_test`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_total:  (489, 3)\n",
      "y_total:  (489L,)\n",
      "X_train:  (391, 3)\n",
      "X_test:  (98, 3)\n",
      "y_train:  (391L,)\n",
      "y_test:  (98L,)\n",
      "Training and testing split was successful.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# TODO: Shuffle and split the data into training and testing subsets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, prices, train_size = 0.8, random_state=0)\n",
    "\n",
    "print \"X_total: \", features.shape\n",
    "print \"y_total: \", prices.shape\n",
    "print \"X_train: \", X_train.shape\n",
    "print \"X_test: \", X_test.shape\n",
    "print \"y_train: \", y_train.shape\n",
    "print \"y_test: \", y_test.shape\n",
    "# Success\n",
    "print \"Training and testing split was successful.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3- 训练及测试\n",
    "*将数据集按一定比例分为训练用的数据集和测试用的数据集对学习算法有什么好处？*\n",
    "\n",
    "**提示：** 如果没有数据来对模型进行测试，会出现什么问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案: ** 不考虑交叉验证中用到的验证数据集，有以下结论：\n",
    "- **训练数据集**用来建立和完善模型。模型的训练误差可以指示模型是否欠拟合，如果模型的训练误差很大，就说明模型过于简单，连该学的特征和关系还没有学会。\n",
    "- **测试数据集**则主要用来评估模型。模型针对测试数据集的误差可以指示模型是否过拟合，如果模型的训练误差极小，但测试数据集的误差较大，就说明模型过于复杂，把不应该学的也学了。\n",
    "- 模型存在的根本目的，是为了预测未来的未知数据集。但是由于我们不可能未卜先知，因此我们不可能用未知的数据集来评估模型，我们只能使用已知且尚未用于训练模型的数据集（即测试数据）来评估模型，并用**测试数据的误差**来近似未知数据集的**泛化误差**。\n",
    "\n",
    "综上所述，如果模型未经测试，将不知道模型的泛化能力如何（有可能已经过拟合）。\n",
    "\n",
    "将数据集拆分为训练用和测试用可以确保训练和测试是完全独立的，即测试数据完全没有参与建立模型的过程，这样模型在测试集下的性能才是客观的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## 分析模型的表现\n",
    "在项目的第三部分，我们来看一下几个模型针对不同的数据集在学习和测试上的表现。另外，你需要专注于一个特定的算法，用全部训练集训练时，提高它的`'max_depth'` 参数，观察这一参数的变化如何影响模型的表现。把你模型的表现画出来对于分析过程十分有益。可视化可以让我们看到一些单看结果看不到的行为。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线\n",
    "下方区域内的代码会输出四幅图像，它们是一个决策树模型在不同最大深度下的表现。每一条曲线都直观的显示了随着训练数据量的增加，模型学习曲线的训练评分和测试评分的变化。注意，曲线的阴影区域代表的是该曲线的不确定性（用标准差衡量）。这个模型的训练和测试部分都使用决定系数R<sup>2</sup>来评分。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形回答下面的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jWnqw1Ga9x8aYnSJyO/B7x/L5HDaYw/8Ae4wxc4wxr4vI/2H7AM0EXMvTvtg28KfGmLUi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3P+L6iJ8XF/lMd73jOZWBb2uM23926mY+67b7K3szCmt///7N15lFxXfS/67665uru6W1K3ph4k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4ab438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce learning curves for varying training set sizes and maximum depths\n",
    "vs.ModelLearning(features, prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 学习数据\n",
    "*选择上述图像中的其中一个，并给出其最大深度。随着训练数据量的增加，训练曲线的评分有怎样的变化？测试曲线呢？如果有更多的训练数据，是否能有效提升模型的表现呢？*\n",
    "**提示：**学习曲线的评分是否最终会收敛到特定的值？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案: **\n",
    "\n",
    "- 对于`max_depth = 3`的图，随着训练数据量的增加，训练曲线的评分从1逐渐趋近于0.8，测试曲线也从0逐渐趋近于0.8。似乎即使有更多的数据，也不能让这两个分数有太大变化了，因为收敛值就是0.8左右，意思就是这个深度的决策树只能达到这个水平了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 复杂度曲线\n",
    "下列代码内的区域会输出一幅图像，它展示了一个已经经过训练和验证的决策树模型在不同最大深度条件下的表现。这个图形将包含两条曲线，一个是训练的变化，一个是测试的变化。跟**学习曲线**相似，阴影区域代表该曲线的不确定性，模型训练和测试部分的评分都用的 `performance_metric` 函数。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形并回答下面的两个问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KAV8gH+xijCGRSbC2cy1grV2NDFXGm1KK3nPAfBGZixW7jwEf77fNRuAU4HER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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd32c4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.ModelComplexity(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5- 偏差与方差之间的权衡取舍\n",
    "*当模型以最大深度 1训练时，模型的预测是出现很大的偏差还是出现了很大的方差？当模型以最大深度10训练时，情形又如何呢？图形中的哪些特征能够支持你的结论？*\n",
    "  \n",
    "**提示：** 你如何得知模型是否出现了偏差很大或者方差很大的问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案: **\n",
    "- 模型以最大深度1训练时，训练的分数和测试的分数都在0.4左右。训练的分数低，说明模型过于简单，该学的还学的不够，所以是偏差大（bias）。\n",
    "- 模型以最大深度10训练时，训练分数已经接近满分，但是测试分数相对最大深度9的分数有所下降，变成了0.7。训练分数极好但测试分数不尽人意，说明模型过于复杂，学的太多了，把训练样本里不该学的东西也学了，逐渐失去了泛化能力，因此测试样本分就低了，所以是方差大（variance）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6- 最优模型的猜测\n",
    "*你认为最大深度是多少的模型能够最好地对未见过的数据进行预测？你得出这个答案的依据是什么？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **答案: **\n",
    "从图中看应该是`max_depth = 4`的模型。此时的验证集的分数达到最高，再大的话分数就开始逐渐降低。因此这个深度的模型最好。\n",
    "\n",
    "后来把整体代码全单独择出来在PyCharm里跑了几次，发现有小概率会出现最优解返回为5的情况，这取决于对数据集划分后数据集的分布情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "## 评价模型表现\n",
    "在这个项目的最后，你将自己建立模型，并使用最优化的`fit_model`函数，基于客户房子的特征来预测该房屋的价值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7- 网格搜索（Grid Search）\n",
    "*什么是网格搜索法？如何用它来优化学习算法？*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答: **\n",
    "网格搜索本质上是利用**穷举的思想**尝试不同的参数组合来确定最优模型。不过具体到实现上，网格搜索并不是完全的穷举（那样运算量太大了），而是：\n",
    "\n",
    "- Step #1：让用户直接指明要搜索哪几个参数，并限定这些个参数要搜索的取值范围（这里的参数指的是**模型自身的参数**，而不是训练数据的特征参数，对于决策树来说主要是树的深度，对于神经网络来说主要是隐藏层数等），这样网格搜索就可以根据这些信息构造出来一个搜索空间（本质上是这些参数取值的排列组合，每个组合都对应一个模型）。\n",
    "\n",
    "\n",
    "- Step #2：然后再用训练数据集来跑这些模型，选出训练出来的模型中评分最好的作为最终模型。其中这里所说的评分对于分类模型可能是F1 Score，对于回归模型可能是$R^2$。这种“训练很多模型然后挑选最优模型的过程”，就是模型参数本身的调优过程，由于本质上这是寻找参数分布规律（即参数的参数）的操作，因此也被称为超参数搜索（Hyper-parameter Search）。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8- 交叉验证\n",
    "*什么是K折交叉验证法（k-fold cross-validation）？优化模型时，使用这种方法对网格搜索有什么好处？网格搜索是如何结合交叉验证来完成对最佳参数组合的选择的？* \n",
    "\n",
    "**提示：** 跟为何需要一组测试集的原因差不多，网格搜索时如果不使用交叉验证会有什么问题？GridSearchCV中的[`'cv_results'`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)属性能告诉我们什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案: 三个问题，依次回答如下。**\n",
    "\n",
    "--------\n",
    "\n",
    "**问：什么是K折交叉验证法（k-fold cross-validation）？**\n",
    "\n",
    "- 答：在解释K折交叉验证法之前，首先让我们来看看没有交叉验证法是什么样的。在没有交叉验证的时候，机器学习的数据集只分为训练集和测试集。用训练集训练出来的模型就已经是最终模型了，再把测试集放到我们的模型中跑来评估模型的泛化性能。要特别注意的是，这时候不管测试出来的结果好与坏，都已经成定局了，我们不能再用测试集来修正模型，那样就是作弊了，一方面违反了测试集不能参与模型构建和训练的规定，一方面手头再也没有模型没用过的数据了。此时的测试集也就变相成为训练集，得出的性能评分只能评估bias不能评估variance，即使模型出现过拟合也意识不到。**那么如何做到既不动测试集，又能够评估模型的泛化性能呢？**为了避免上面这种没有回旋余地的窘境，解决方案就是我们把原训练集`Train`进一步拆分为`Train'`和`Validation`（即新训练集和验证集），其中验证集用来评估模型的泛化能力。这样，我们就成功做到在未使用测试集的前提下对模型的bias（用训练集）和variance（用验证集）进行评估。\n",
    "\n",
    "```\n",
    "\n",
    "┌────────────────────────────────────┬────────────┐\n",
    "│                Train               │    Test    │\n",
    "└────────────────────────────────────┴────────────┘\n",
    "↑                                    ↑\n",
    "┌───────────────────────┬────────────┐\n",
    "│         Train'        │ Validation │\n",
    "└───────────────────────┴────────────┘\n",
    "                        Pretend As Test\n",
    "\n",
    "```\n",
    "\n",
    "而**K折交叉验证法**就是在此基础上，将原训练集进一步等分为K段互斥子集，构造K套不同的训练集+验证集组合，其中每个组合依次使用K段中的1段作为验证集，剩下K-1段作为训练集。然后将模型用这K套数据集进行训练/评估，最后将这K次训练后用相应验证集评估得到的K个性能分数求平均作为模型的整体性能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "**问：优化模型时，使用K折交叉验证法对网格搜索有什么好处？**\n",
    "\n",
    "- 答：假设我们不用网格搜索 + K-Fold CV，而是直接用网格搜索 + Hold-out，可能会导致网格搜索得到的所谓“最优模型”并不是真正的最优模型，而如果用了K-Fold CV就可以减少这种可能性。这背后的原因在于数据集的分布和划分。**机器学习所用的数据集有以下2个大前提：**\n",
    " \n",
    " \n",
    "- **数据集本身能够代表真实世界中事物的趋势和分布**。具体可以细分为两个维度：\n",
    "    \n",
    "    - **纵向（时间维）**：比如波士顿房价数据集，只能反应数据集采集的那个年代波士顿的房价，用于预测现在的波士顿肯定不现实。因为它不能代表当前波士顿房价的趋势和分布特性。\n",
    "    \n",
    "    - **横向（特征维）**：数据集所提供的特征应该尽可能的全面。真实世界中，影响一个事物发展趋势的因素是相当复杂和多样的，如果数据集包含的特征过少，那么无论你怎么训练，都不可能进行准确的预测，因为忽略了一些重要因素。\n",
    "  \n",
    "  \n",
    "- **数据集内训练集和验证集的划分应尽可能保证独立同分布（I.I.D）**。如果两者分布是一致的，那么网格搜索出的最优解就是真的最优解。如果两者的分布差别很大，比如训练集中A特征特别重要，但验证集中B特征特别重要，那么即使用网格搜索得到的“最优解”性能可能也很差，原因是它训练出来的模型只能用于预测**符合训练集统计特征的数据**，用在验证集上就不好使了，因此并不是针对整体数据集来说的最优解。K-Fold CV通过多次尝试求平均从统计角度上规避了这种情况的出现，搭配网格搜索，可以确保网格搜索的解是真的最优解。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "**问：网格搜索是如何结合交叉验证来完成对最佳参数组合的选择的？**\n",
    "\n",
    "- 答：网格搜索负责hold住模型这边的参数调优，交叉验证负责hold住数据集这边确保分布平均打分。两者嵌套在一起，完成最佳参数组合的选择。（见下面我简单写的伪代码）\n",
    "\n",
    "```\n",
    "伪代码：\n",
    "\n",
    "1. 准备网格搜索：给网格搜索指明要搜索的参数和参数取值区间，假设一共有<M>种排列组合。\n",
    "\n",
    "2. 准备数据集：将数据集用K-Fold CV划分成<K>套训练集+验证集。\n",
    "\n",
    "3. for 模型参数组合i in range(M)：\n",
    "\n",
    "      for 训练集j in range(K)：\n",
    "\n",
    "          训练模型\n",
    "\n",
    "          用验证集j对模型评分存入score[j]\n",
    "\n",
    "      end\n",
    "\n",
    "      将score数组的平均值存入mean_score[i]\n",
    "\n",
    "   end\n",
    "\n",
    "4. 选择mean_score最大的模型参数组合作为最终模型。\n",
    "\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "`cv_results_`不仅包含用户配置的搜索空间，也包含通过网格搜索加K-Fold得到的所有结论，包括各个split的训练得分和测试得分等等。\n",
    "\n",
    "以下面代码为例，可以看到sklearn中的网格搜索模型是如何具体构造出来的：\n",
    "```\n",
    "grid = GridSearchCV(estimator=regressor, param_grid=params, scoring=scoring_fnc, cv=cv_sets)\n",
    "```\n",
    "- `estimator` 要用的机器学习模型对象：这里实际上用的还是决策树。\n",
    "- `param_grid` 参数网格词典：用户需要提供他认为对该模型调优很重要的参数以及可能的取值（潜在的最优参数区间），这样就限定了网格搜索只在一个有限的空间内搜索，不会太耗费时间。词典的每一个键值对都是一个参与搜索的参数维度。键值对的键表示这个参数的名称，在这里用的是是决策树的深度。键值对的值是这个参数所需要搜索的取值列表，这里选择的要尝试的范围是1到10。参数的具体取值可以通过先画学习曲线来确定下大致的取值范围。\n",
    "- `scoring` 评分机制：指定评价模型好坏的评估参数，这里用的实际上就是$R^2$。\n",
    "- `cv` 交叉验证集：给出用来训练和验证用的数据集。这里使用了ShuffleSplit将原数据的训练集构造成为训练集和验证集。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：训练模型\n",
    "在最后一个练习中，你将需要将所学到的内容整合，使用**决策树演算法**训练一个模型。为了保证你得出的是一个最优模型，你需要使用网格搜索法训练模型，以找到最佳的 `'max_depth'` 参数。你可以把`'max_depth'` 参数理解为决策树算法在做出预测前，允许其对数据提出问题的数量。决策树是**监督学习算法**中的一种。\n",
    "\n",
    "此外，你会发现你的实现使用的是 `ShuffleSplit()` 。它也是交叉验证的一种方式（见变量 `'cv_sets'`）。虽然这不是**问题8**中描述的 K-Fold 交叉验证，这个教程验证方法也很有用！这里 `ShuffleSplit()` 会创造10个(`'n_splits'`)混洗过的集合，每个集合中20%(`'test_size'`)的数据会被用作**验证集**。当你在实现的时候，想一想这跟 K-Fold 交叉验证有哪些相同点，哪些不同点？\n",
    "\n",
    "在下方 `fit_model` 函数中，你需要做的是：\n",
    "- 使用  `sklearn.tree` 中的 [`DecisionTreeRegressor`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) 创建一个决策树的回归函数；\n",
    "  - 将这个回归函数储存到 `'regressor'` 变量中；\n",
    "- 为 `'max_depth'` 创造一个字典，它的值是从1至10的数组，并储存到 `'params'` 变量中；\n",
    "- 使用 `sklearn.metrics` 中的 [`make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html)  创建一个评分函数；\n",
    "  - 将 `performance_metric` 作为参数传至这个函数中；\n",
    "  - 将评分函数储存到 `'scoring_fnc'` 变量中；\n",
    "- 使用 `sklearn.model_selection` 中的 [`GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) 创建一个网格搜索对象；\n",
    "  - 将变量`'regressor'`, `'params'`, `'scoring_fnc'`, 和 `'cv_sets'` 作为参数传至这个对象中；\n",
    "  - 将 `GridSearchCV` 存到 `'grid'` 变量中。\n",
    "  \n",
    "如果有同学对python函数如何传递多个参数不熟悉，可以参考这个MIT课程的[视频](http://cn-static.udacity.com/mlnd/videos/MIT600XXT114-V004200_DTH.mp4)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def fit_model(X, y):\n",
    "    \"\"\" Performs grid search over the 'max_depth' parameter for a \n",
    "        decision tree regressor trained on the input data [X, y]. \"\"\"\n",
    "    \n",
    "    # Create cross-validation sets from the training data\n",
    "    cv_sets = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)\n",
    "\n",
    "    # TODO: Create a decision tree regressor object\n",
    "    regressor = DecisionTreeRegressor(random_state=0)\n",
    "\n",
    "    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10\n",
    "    params = {'max_depth' : np.arange(1, 11)}\n",
    "\n",
    "    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' \n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "\n",
    "    # 注意一定要Shift+Tab查看函数签名，特别是对于这种参数巨多而且全带默认值的函数，不指明参数名就赋值很容易出奇怪的错误。\n",
    "    # 如果最后一个参数不写成`cv=cv_sets`，那么会被识别成fit_params这个参数，导致报一堆错。\n",
    "    grid = GridSearchCV(regressor, params, scoring_fnc, cv=cv_sets)\n",
    "\n",
    "    # Fit the grid search object to the data to compute the optimal model\n",
    "    grid.fit(X, y)\n",
    "\n",
    "    # Return the optimal model after fitting the data\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 做出预测\n",
    "当我们用数据训练出一个模型，它现在就可用于对新的数据进行预测。在决策树回归函数中，模型已经学会对新输入的数据*提问*，并返回对**目标变量**的预测值。你可以用这个预测来获取数据未知目标变量的信息，这些数据必须是不包含在训练数据之内的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9- 最优模型\n",
    "*最优模型的最大深度（maximum depth）是多少？此答案与你在**问题 6**所做的猜测是否相同？*\n",
    "\n",
    "运行下方区域内的代码，将决策树回归函数代入训练数据的集合，以得到最优化的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'max_depth' is 4 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# Fit the training data to the model using grid search\n",
    "reg = fit_model(features, prices)\n",
    "\n",
    "# Produce the value for 'max_depth'\n",
    "print \"Parameter 'max_depth' is {} for the optimal model.\".format(reg.get_params()['max_depth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: 最优模型的最大深度是4，和图上看的基本上接近。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 预测销售价格\n",
    "想像你是一个在波士顿地区的房屋经纪人，并期待使用此模型以帮助你的客户评估他们想出售的房屋。你已经从你的三个客户收集到以下的资讯:\n",
    "\n",
    "| 特征 | 客戶 1 | 客戶 2 | 客戶 3 |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| 房屋内房间总数 | 5 间房间 | 4 间房间 | 8 间房间 |\n",
    "| 社区贫困指数（％被认为是贫困阶层） | 17% | 32% | 3% |\n",
    "| 邻近学校的学生-老师比例 | 15：1 | 22：1 | 12：1 |\n",
    "\n",
    "*你会建议每位客户的房屋销售的价格为多少？从房屋特征的数值判断，这样的价格合理吗？* \n",
    "\n",
    "**提示：**用你在**分析数据**部分计算出来的统计信息来帮助你证明你的答案。\n",
    "\n",
    "运行下列的代码区域，使用你优化的模型来为每位客户的房屋价值做出预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted selling price for Client 1's home: $408,800.00\n",
      "Predicted selling price for Client 2's home: $231,253.45\n",
      "Predicted selling price for Client 3's home: $938,053.85\n"
     ]
    }
   ],
   "source": [
    "# Produce a matrix for client data\n",
    "client_data = [[5, 17, 15], # Client 1\n",
    "               [4, 32, 22], # Client 2\n",
    "               [8, 3, 12]]  # Client 3\n",
    "\n",
    "# Show predictions\n",
    "for i, price in enumerate(reg.predict(client_data)):\n",
    "    print \"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案: **\n",
    "建议销售价：\n",
    "```\n",
    "- Client A: $408,800\n",
    "- Client B: $231,253\n",
    "- Client C: $938,053\n",
    "```\n",
    "价格挺合理的。\n",
    "\n",
    "\n",
    "- 首先可以将结果与一开始得到的所有房价数据的统计信息对比如下，可见房价的区间为105000至1024800，三个客户的建议价格都在这个取值区间内。\n",
    "```\n",
    "Minimum price: $105,000.00\n",
    "Maximum price: $1,024,800.00\n",
    "Mean price: $454,342.94\n",
    "Median price $438,900.00\n",
    "Standard deviation of prices: $165,171.13\n",
    "```\n",
    "\n",
    "- 然后可以有针对性的看每个顾客的房型对应的房价数据的分布\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count         5.000000\n",
      "mean     357420.000000\n",
      "std      166334.052437\n",
      "min      184800.000000\n",
      "25%      249900.000000\n",
      "50%      289800.000000\n",
      "75%      485100.000000\n",
      "max      577500.000000\n",
      "Name: MEDV, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>3.561</td>\n",
       "      <td>7.12</td>\n",
       "      <td>20.2</td>\n",
       "      <td>577500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>3.863</td>\n",
       "      <td>13.33</td>\n",
       "      <td>20.2</td>\n",
       "      <td>485100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>4.138</td>\n",
       "      <td>37.97</td>\n",
       "      <td>20.2</td>\n",
       "      <td>289800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>4.368</td>\n",
       "      <td>30.63</td>\n",
       "      <td>20.2</td>\n",
       "      <td>184800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>4.138</td>\n",
       "      <td>23.34</td>\n",
       "      <td>20.2</td>\n",
       "      <td>249900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RM  LSTAT  PTRATIO      MEDV\n",
       "353  3.561   7.12     20.2  577500.0\n",
       "355  3.863  13.33     20.2  485100.0\n",
       "357  4.138  37.97     20.2  289800.0\n",
       "367  4.368  30.63     20.2  184800.0\n",
       "389  4.138  23.34     20.2  249900.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Client A has Room == 4 由于相应样本较少，可以同时看一下原始数据及房价的统计数据\n",
    "a = data[(data['RM'] > 3.5) & (data['RM'] < 4.5)]\n",
    "print a.MEDV.describe()\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到数据集中4间房左右的房价均值为35万，标准差为16万，考虑到另外两种特征的分布，因此Client A建议40万是合适的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        36.000000\n",
       "mean     293416.666667\n",
       "std      101980.105903\n",
       "min      105000.000000\n",
       "25%      217350.000000\n",
       "50%      296100.000000\n",
       "75%      374325.000000\n",
       "max      497700.000000\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Client B has Room == 5\n",
    "data[(data['RM'] > 4.5) & (data['RM'] < 5.5)].MEDV.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到数据集中5间房左右的房价均值为29万，标准差为10万，考虑到另外两种特征的分布，因此Client B建议23万是合适的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    7.000000e+00\n",
      "mean     9.081000e+05\n",
      "std      9.151164e+04\n",
      "min      7.896000e+05\n",
      "25%      8.442000e+05\n",
      "50%      8.988000e+05\n",
      "75%      9.775500e+05\n",
      "max      1.024800e+06\n",
      "Name: MEDV, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>8.069</td>\n",
       "      <td>4.21</td>\n",
       "      <td>18.0</td>\n",
       "      <td>812700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>8.266</td>\n",
       "      <td>4.14</td>\n",
       "      <td>17.4</td>\n",
       "      <td>940800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>8.040</td>\n",
       "      <td>3.13</td>\n",
       "      <td>17.4</td>\n",
       "      <td>789600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>8.337</td>\n",
       "      <td>2.47</td>\n",
       "      <td>17.4</td>\n",
       "      <td>875700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>8.247</td>\n",
       "      <td>3.95</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1014300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>8.259</td>\n",
       "      <td>3.54</td>\n",
       "      <td>19.1</td>\n",
       "      <td>898800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>8.398</td>\n",
       "      <td>5.91</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1024800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RM  LSTAT  PTRATIO       MEDV\n",
       "97   8.069   4.21     18.0   812700.0\n",
       "217  8.266   4.14     17.4   940800.0\n",
       "218  8.040   3.13     17.4   789600.0\n",
       "224  8.337   2.47     17.4   875700.0\n",
       "225  8.247   3.95     17.4  1014300.0\n",
       "245  8.259   3.54     19.1   898800.0\n",
       "253  8.398   5.91     13.0  1024800.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Client C has Room == 9 由于样本较少，可以看一下8间房左右的房价统计数据\n",
    "c = data[(data['RM'] > 8) & (data['RM'] < 10)]\n",
    "print c.MEDV.describe()\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到数据集中8间房左右的房价均值为90万，标准差为9万，考虑到另外两种特征的分布，因此Client C建议93万是合适的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ！也可以使用KNN（K近邻）来验证房价数据预测的靠谱程度\n",
    "\n",
    "将Client的特征信息用于寻找最近的5个数据点，然后看看这几个点的房价和预测值的差距是否合理，就能知道大概拟合的怎么样了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[101 168 382 232 381]]\n",
      "以第一个Client为例，其各项特征值为：\n",
      "[5, 17, 15] 房价预测 = 408800\n",
      "KNN寻找到的最近训练数据点的各项特征值为：\n",
      "5.628 16.65 14.7 房价预测 = 327600.0\n",
      "5.272 16.14 14.7 房价预测 = 275100.0\n",
      "6.004 17.1 15.2 房价预测 = 396900.0\n",
      "5.79 15.84 16.0 房价预测 = 426300.0\n",
      "5.951 17.92 16.4 房价预测 = 451500.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "nb = NearestNeighbors()\n",
    "nb.fit(X_train)\n",
    "\n",
    "# Get the nearest 5 neighbors as reference\n",
    "candidates = nb.kneighbors([client_data[0]], n_neighbors=5, return_distance=False) \n",
    "print candidates\n",
    "print \"以第一个Client为例，其各项特征值为：\"\n",
    "print client_data[0], \"房价预测 = 408800\"\n",
    "print \"KNN寻找到的最近训练数据点的各项特征值为：\"\n",
    "for i in range(5):\n",
    "    c = X_train.iloc[candidates[0][i]]\n",
    "    print float(c.RM), float(c.LSTAT), float(c.PTRATIO), \"房价预测 =\", float(y_train.iloc[candidates[0][i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意由于之前的train_test_split已经将data的数据顺序重新采样打乱生成为X_train和y_train，因此这里面的存储顺序和主键顺序已经不再一致。所以使用KNN找到了最近五个训练数据点的编号是其存储顺序的编号，而不是主键。因此需要用iloc访问，而不是loc。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 敏感度\n",
    "\n",
    "一个最优的模型不一定是一个健壮模型。有的时候模型会过于复杂或者过于简单，以致于难以泛化新增添的数据；有的时候模型采用的学习算法并不适用于特定的数据结构；有的时候样本本身可能有太多噪点或样本过少，使得模型无法准确地预测目标变量。这些情况下我们会说模型是欠拟合的。执行下方区域中的代码，采用不同的训练和测试集执行 `fit_model` 函数10次。注意观察对一个特定的客户来说，预测是如何随训练数据的变化而变化的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1: $391,183.33\n",
      "Trial 2: $424,935.00\n",
      "Trial 3: $415,800.00\n",
      "Trial 4: $420,622.22\n",
      "Trial 5: $418,377.27\n",
      "Trial 6: $411,931.58\n",
      "Trial 7: $399,663.16\n",
      "Trial 8: $407,232.00\n",
      "Trial 9: $351,577.61\n",
      "Trial 10: $413,700.00\n",
      "\n",
      "Range in prices: $73,357.39\n"
     ]
    }
   ],
   "source": [
    "vs.PredictTrials(features, prices, fit_model, client_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 11 - 实用性探讨\n",
    "*简单地讨论一下你建构的模型能否在现实世界中使用？* \n",
    "\n",
    "**提示：** 回答几个问题，并给出相应结论的理由：\n",
    "- *1978年所采集的数据，在今天是否仍然适用？*\n",
    "- *数据中呈现的特征是否足够描述一个房屋？*\n",
    "- *模型是否足够健壮来保证预测的一致性？*\n",
    "- *在波士顿这样的大都市采集的数据，能否应用在其它乡镇地区？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案: **\n",
    "- 问：1978年所采集的数据，在今天是否仍然适用？\n",
    "\n",
    "    答：可能有一定参考价值，但是随着时间的推移，影响房价的因素也在不断的变化，因此并不能完全适用。\n",
    "   \n",
    "   \n",
    "- 问：数据中呈现的特征是否足够描述一个房屋？\n",
    "\n",
    "    答：应该可以有更多的特征来描述。比如房屋离各种交通枢纽的距离等等。\n",
    "\n",
    "\n",
    "- 问：模型是否足够健壮来保证预测的一致性？\n",
    "    \n",
    "    答：健壮性还可以吧。起码从上面10次预测的结果可以看到取值区间波动在7万美金、方差为2万美金左右，这对于40万美金左右的房子来说还可以吧。\n",
    "\n",
    "\n",
    "- 问：在波士顿这样的大都市采集的数据，能否应用在其它乡镇地区？\n",
    "    \n",
    "    答：恐怕不行。因为采集的数据很大程度与区域强相关，变了区域规律也就变了，因此模型也就不适用了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可选问题 - 预测北京房价\n",
    "\n",
    "（本题结果不影响项目是否通过）通过上面的实践，相信你对机器学习的一些常用概念有了很好的领悟和掌握。但利用70年代的波士顿房价数据进行建模的确对我们来说意义不是太大。现在你可以把你上面所学应用到北京房价数据集中`bj_housing.csv`。\n",
    "\n",
    "免责声明：考虑到北京房价受到宏观经济、政策调整等众多因素的直接影响，预测结果仅供参考。\n",
    "\n",
    "这个数据集的特征有：\n",
    "- Area：房屋面积，平方米\n",
    "- Room：房间数，间\n",
    "- Living: 厅数，间\n",
    "- School: 是否为学区房，0或1\n",
    "- Year: 房屋建造时间，年\n",
    "- Floor: 房屋所处楼层，层\n",
    "\n",
    "目标变量：\n",
    "- Value: 房屋人民币售价，万\n",
    "\n",
    "你可以参考上面学到的内容，拿这个数据集来练习数据分割与重排、定义衡量标准、训练模型、评价模型表现、使用网格搜索配合交叉验证对参数进行调优并选出最佳参数，比较两者的差别，最终得出最佳模型对验证集的预测分数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NmjWJX3/9dVL4OomJiaFzzjmnCGDs2LGl69ati9hM7Oyzz94NcMQRR5Ru3Lgx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3e5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RLPoM8m+55aFBbZ/R47oDEwkbZBKN5i0nN8lyWdmgabV80V4iL7xM2fOrKHt+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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3e5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final model has a max_depth of  9\n",
      "Model Score on Testing Set:  0.484820304883\n"
     ]
    }
   ],
   "source": [
    "# Beijing Housing Price Prediction Model\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "\n",
    "data = pd.read_csv('bj_housing.csv')\n",
    "prices = data['Value']\n",
    "features = data.drop('Value', axis=1)    \n",
    "X_train, X_test, y_train, y_test = train_test_split(features, prices, train_size=0.8)\n",
    "\n",
    "# Train the model, and using GridSearch + K-Fold CV for model parameter tuning.\n",
    "def fit_model(X, y):\n",
    "    cv_sets = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\n",
    "    regressor = DecisionTreeRegressor(random_state=0)\n",
    "    params = {'max_depth': np.arange(1, 11)}\n",
    "    scoring_fnc = make_scorer(r2_score)\n",
    "    grid = GridSearchCV(estimator=regressor, param_grid=params, scoring=scoring_fnc, cv=cv_sets)\n",
    "    grid.fit(X, y)\n",
    "    return grid.best_estimator_\n",
    "\n",
    "\n",
    "model = fit_model(X_train, y_train)\n",
    "md = model.get_params()['max_depth']\n",
    "vs.ModelLearning(features, prices)\n",
    "vs.ModelComplexity(X_train, y_train)\n",
    "print \"The final model has a max_depth of \", md\n",
    "\n",
    "\n",
    "# Evaluate the final model\n",
    "y_pred = model.predict(pd.DataFrame(X_test))\n",
    "print \"Model Score on Testing Set: \", r2_score(y_pred, y_test)\n",
    "\n",
    "\n",
    "vs.PredictTrials(features, prices, fit_model, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你成功的用新的数据集构建了模型了吗？他能对测试数据进行验证吗？它的表现是否符合你的预期？交叉验证是否有助于提升你模型的表现？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案：**\n",
    "\n",
    "- 由上图可见，预测北京房价的模型在测试数据集下的性能分数是0.48，达到这个性能所用的决策树模型深度是9。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model tree max depth Statistics over 10 run: \n",
      "count    10.000000\n",
      "mean      6.500000\n",
      "std       2.173067\n",
      "min       3.000000\n",
      "25%       5.250000\n",
      "50%       6.000000\n",
      "75%       8.750000\n",
      "max       9.000000\n",
      "dtype: float64\n",
      "\n",
      "Model R2 Score Statistics over 10 run: \n",
      "count    10.000000\n",
      "mean      0.450899\n",
      "std       0.222173\n",
      "min      -0.028896\n",
      "25%       0.341710\n",
      "50%       0.477015\n",
      "75%       0.625357\n",
      "max       0.700067\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def robust_test(features, prices, models, iteration):\n",
    "    depth = []\n",
    "    score = []\n",
    "    for x in range(iteration):\n",
    "        X_train, X_test, y_train, y_test = train_test_split(features, prices, train_size=0.8, random_state=x)\n",
    "        model = fit_model(X_train, y_train)\n",
    "        depth.append(model.get_params()['max_depth'])\n",
    "        y_pred = model.predict(pd.DataFrame(X_test))\n",
    "        score.append(r2_score(y_pred, y_test))\n",
    "    return depth, score\n",
    "\n",
    "(depth, score) = robust_test(features, prices, fit_model, 10)\n",
    "print \"\\nModel tree max depth Statistics over 10 run: \\n\", pd.Series(depth).describe()\n",
    "print \"\\nModel R2 Score Statistics over 10 run: \\n\", pd.Series(score).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**答案：**\n",
    "- 问：它的表现是否符合你的预期？\n",
    "\n",
    "    我多跑了几次，发现模型的性能并不稳定，于是写了一个测试模型鲁棒性能的函数，检查独立构造模型10次中每次最终模型的深度`depth`和针对测试集的分数`score`。然后发现模型最差的时候甚至能够得负分，就是说这时候模型还不如直接对原数据集平均得到的结果准确...不过最好的时候模型还能达到0.7分的高分。考虑到已经使用了GridSearch和K-Fold CV，因此这种不稳定的现象应该不是模型本身参数的问题，也不是数据集本身划分不均匀导致的，而是**数据集提供的特征根本就不够导致的**（详见回答问题8的**机器学习所用的数据集的2个大前提**总结）。影响北京的房价的因素肯定远远不止数据集中这6个特征，还要考虑到政策调控和宏观经济走势的影响。因此这个数据集训练出来的模型的准确度和稳定性都不会太高。\n",
    "\n",
    "\n",
    "- 问：交叉验证是否有助于提升你模型的表现？\n",
    "\n",
    "    使用ShuffleSplit交叉验证还是很重要的，它能确保你在对训练数据集的建模结果更为准确。"
   ]
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    "如果你是从零开始构建机器学习的代码会让你一时觉得无从下手。这时不要着急，你要做的只是查看之前写的代码，把每一行都看明白，然后逐步构建你的模型。当中遇到什么问题也可以在我们论坛寻找答案。也许你会发现你所构建的模型的表现并没有达到你的预期，这说明机器学习并非是一项简单的任务，构建一个表现良好的模型需要长时间的研究和测试。这也是我们接下来的课程中会逐渐学到的。"
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